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      <title>在分布式系统中如何应对网络分区 - 交互式测验</title>
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      </style>
    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>在分布式系统中如何应对网络分区 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
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                    <div id="question-container">
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                    <div id="hint-container" class="hint-container"></div>
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                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
            </div>
          </div>
      </div>

      <script>
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  {
    "question": "根据CAP定理，当分布式系统发生网络分区（Partition Tolerance）时，系统设计者必须在哪两个特性之间做出权衡？",
    "options": [
      "性能 (Performance) 和 可靠性 (Reliability)",
      "一致性 (Consistency) 和 可用性 (Availability)",
      "可扩展性 (Scalability) 和 持久性 (Durability)",
      "延迟 (Latency) 和 吞吐量 (Throughput)"
    ],
    "correctAnswerIndex": 1,
    "explanation": "CAP定理明确指出，在分布式系统中，一致性（Consistency）、可用性（Availability）和分区容错性（Partition Tolerance）这三个特性最多只能同时满足两个。由于网络分区在分布式环境中是不可避免的，因此必须保证分区容错性（P）。这就迫使系统设计者必须在剩下的一致性（C）和可用性（A）之间进行选择和权衡。",
    "source": "文档中「一致性与可用性的权衡」->「1. CAP定理概述」部分",
    "hint": "文档中提到一个核心定理，它由三个单词的首字母组成，并阐述了在网络故障时系统必须做出的选择。"
  },
  {
    "question": "当电商平台的支付服务因网络分区而暂时不可用时，系统仍然允许用户浏览商品和查看购物车，但禁用了下单购买功能。这种策略属于哪一种服务降级方式？",
    "options": [
      "服务级别降级",
      "自动故障转移",
      "功能降级（功能裁剪）",
      "数据冗余"
    ],
    "correctAnswerIndex": 2,
    "explanation": "功能降级（特别是功能裁剪）是指在系统出现问题时，通过暂时禁用或简化部分非核心功能来保证核心服务的可用性。文档中明确举例：“电商平台在支付服务出现问题时，允许用户浏览商品和查看购物车，但禁用购买功能”，这正是功能降级的典型应用。",
    "source": "文档中「服务可用性与降级策略」->「3. 常见的降级策略」->「功能降级」部分",
    "hint": "思考一下，当系统无法提供完整服务时，为了维持基本的用户体验，它会选择“牺牲”哪一部分功能？"
  },
  {
    "question": "在分布式系统中，哪种数据复制模型提供了更高的可用性，但同时也带来了更高的一致性管理复杂度和冲突解决难题？",
    "options": [
      "主从复制 (Master-Slave Replication)",
      "异步复制 (Asynchronous Replication)",
      "主主复制 (Master-Master Replication)",
      "增量备份 (Incremental Backup)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "主主复制（Master-Master Replication）模型允许多个主节点都能处理写操作，这提高了系统的可用性，因为单个主节点的故障不会导致写操作中断。然而，由于多个节点可以同时写入，数据同步和冲突解决的复杂度也随之显著增加。",
    "source": "文档中「系统设计与架构」->「1. 数据复制与一致性」->「数据复制」部分",
    "hint": "考虑哪种复制模型中存在多个可以接受写入操作的节点，这会带来什么好处和挑战？"
  },
  {
    "question": "文档中提到的 Two-Phase Commit (2PC) 协议主要用于解决什么问题，并且在网络分区情况下存在什么潜在风险？",
    "options": [
      "解决数据备份问题，风险是备份速度慢。",
      "解决服务发现问题，风险是服务列表不一致。",
      "解决负载均衡问题，风险是流量分配不均。",
      "解决分布式事务的一致性问题，风险是可能导致阻塞。"
    ],
    "correctAnswerIndex": 3,
    "explanation": "两阶段提交（2PC）是用于确保分布式事务原子性和一致性的经典协议。它通过协调者和参与者两个阶段的通信来决定事务是统一提交还是统一回滚。然而，文档中明确指出，如果在协调者做出最终决定后与某些参与者发生网络分区，这些参与者将无法知道最终结果，从而进入阻塞状态，直到通信恢复。",
    "source": "文档中「系统设计与架构」->「1. 数据复制与一致性」->「一致性协议」部分",
    "hint": "这个协议涉及一个“协调者”和多个“参与者”，旨在确保一个跨多个系统的操作要么全部成功，要么全部失败。想一想，如果协调者在关键时刻“失联”会发生什么？"
  },
  {
    "question": "以下哪项不是文档中描述的用于“故障检测”的常见方法？",
    "options": [
      "心跳机制 (Heartbeat)",
      "健康检查 (Health Check)",
      "事务回滚 (Transaction Rollback)",
      "日志分析 (Log Analysis)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "心跳机制、健康检查和日志分析都是文档中明确提到的用于主动或被动识别系统故障的“故障检测”方法。而“事务回滚”是一种“故障恢复”策略，它是在检测到故障（如事务处理失败）后，为保证数据一致性而采取的恢复措施，而不是用于发现故障本身。",
    "source": "文档中「故障检测与恢复」->「1. 故障检测」与「2. 故障恢复」部分",
    "hint": "这个问题要求区分“发现问题”的手段和“解决问题”的手段。哪个选项更像是一个补救措施，而不是一个监控手段？"
  },
  {
    "question": "根据文档中的实际案例分析，哪个系统被设计为优先保证“高可用性”和“最终一致性”（AP模型），而哪个系统则提供了“强一致性”（CA/CP模型）？",
    "options": [
      "高可用性: Netflix; 强一致性: Amazon Web Services",
      "高可用性: Google Spanner; 强一致性: Amazon DynamoDB",
      "高可用性: Amazon DynamoDB; 强一致性: Google Spanner",
      "高可用性: Raft协议; 强一致性: Paxos协议"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档在多个章节中都引用了这两个案例。明确指出 Amazon DynamoDB 采用了最终一致性模型，以确保高可用性和分区容忍性（AP）。相反，Google Spanner 通过分布式共识协议和全球时间同步，提供了强一致性，适用于金融服务等对数据一致性要求极高的场景（通常被视为CP系统，但在无分区时可看作CA）。",
    "source": "文档中「一致性与可用性的权衡」->「4. 实际案例」部分 (在「系统设计与架构」部分也有提及)",
    "hint": "回想一下文档中提到的两个著名的大型分布式数据库系统案例，它们分别代表了CAP权衡的哪两个不同方向？"
  }
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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
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            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
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                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
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                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
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                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
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                '<div class="score-card score-details">' +
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                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
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                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  